Dennis Jensen, MD.
Ontology highlight
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PROVIDER: S-EPMC6945236 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The main aim of this paper is to give an improvement of the recent result on the sharpness of the Jensen inequality. The results given here are obtained using different Green functions and considering the case of the real Stieltjes measure, not necessarily positive. Finally, some applications involving various types of f-divergences and Zipf–Mandelbrot law are presented.
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Project description:MotivationThe evolution of complex diseases can be modeled as a time-dependent nonlinear dynamic system, and its progression can be divided into three states, i.e., the normal state, the pre-disease state and the disease state. The sudden deterioration of the disease can be regarded as the state transition of the dynamic system at the critical state or pre-disease state. How to detect the critical state of an individual before the disease state based on single-sample data has attracted many researchers' attention.MethodsIn this study, we proposed a novel approach, i.e., single-sample-based Jensen-Shannon Divergence (sJSD) method to detect the early-warning signals of complex diseases before critical transitions based on individual single-sample data. The method aims to construct score index based on sJSD, namely, inconsistency index (ICI).ResultsThis method is applied to five real datasets, including prostate cancer, bladder urothelial carcinoma, influenza virus infection, cervical squamous cell carcinoma and endocervical adenocarcinoma and pancreatic adenocarcinoma. The critical states of 5 datasets with their corresponding sJSD signal biomarkers are successfully identified to diagnose and predict each individual sample, and some "dark genes" that without differential expressions but are sensitive to ICI score were revealed. This method is a data-driven and model-free method, which can be applied to not only disease prediction on individuals but also targeted drug design of each disease. At the same time, the identification of sJSD signal biomarkers is also of great significance for studying the molecular mechanism of disease progression from a dynamic perspective.
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2023-03-19 | GSE216662 | GEO
Project description:Dating of wood is a major task in historical research, archaeology and paleoclimatology. Currently, the most important dating techniques are dendrochronology and radiocarbon dating. Our approach is based on molecular decay over time under specific preservation conditions. In the models presented here, construction wood, cold soft waterlogged wood and wood from living trees are combined. Under these conditions, molecular decay as a usable clock for dating purposes takes place with comparable speed. Preservation conditions apart from those presented here are not covered by the model and cannot currently be dated with this method. For example, samples preserved in a clay matrix seem not to fit into the model. Other restrictions are discussed in the paper. One model presented covers 7,500 years with a root mean square error (RMSE) of 682 years for a single measurement. Another model reduced to the time period of the last 800 years results in a RMSE of 92 years. As multiple measurements can be performed on a single object, the total error for the whole object will be even lower.
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